The emergence and spread of antimicrobial-resistant bacteria (ARB) is one of the most serious public health threats. Patients admitted to healthcare institutions are the main reservoirs of ARB. It is estimated that 5-10% of patients develop an infection directly related to their hospitalization, resulting in over 90,000 deaths per year in the US. Approximately 70% of the causative pathogens implicated in these infections are resistant to one or more antimicrobials. Infections caused by ARB are associated with up to 5-times higher mortality rates than infections caused by antimicrobial-susceptible bacteria. To gain a complete understanding of the numerous interrelated variables that contribute to the spread of ARB, integration of individual-level and population-level data is necessary. Biological studies provide essential data to understand behavior at the single patient-level, but the spread of (ARB) requires an understanding of the complex transmission dynamics of ARB between patients and healthcare workers. The goal of this interdisciplinary proposal is to integrate individual-patient data with mathematical modeling to provide population-level analysis of the spread of ARB. Models will be created to characterize the superspreaders of ARB, the subgroup of patients responsible for the majority of ARB spread. Individual-level patient data will be obtained from an extensive integrated on-line medical record system of over one million hospitalized patients. Patient data of bacterial loads of ARB and its correlation with antibiotic exposure, the main risk factor for ARB spread, will be obtained from previous prospective clinical studies. Simulations of the models will be used to predict the effectiveness of preventative strategies. This proposal will enhance the partnership between medicine, public health and mathematics in both research and education. The results will be disseminated broadly through conferences, publications, undergraduate, graduate, and post-graduate education, medical students, nurses and physicians. The proposed work will lead to a better understanding of the factors which contribute to the spread of ARB, and will allow the development and implementation of effective public health policies. [unreadable] [unreadable] [unreadable]